There are large amount of people affected by sleep related conditions who could benefit from knowing more about their sleep habits. Some research indicates that 40% of all American adults suffer from some kind of sleep disorder, while about 70 million Americans are chronically sleep deprived. Many feel that little substantial improvement can be made to correct their problems, since sufferers often do not discuss the problem with their physician.
The current gold standard for sleep research is known as polysomnography (PSG), which involves at least the recording of an electroencephalogram (EEG), a measurement of brain waves, an electrooculogram (EOG), a measurement of muscle activity in the eye area, and an electromyogram (EMG), a measurement of muscle activity in specific areas such as the arm or leg. These waveforms allow a doctor to assess a patient's sleep quality. All of these electrode hook-ups prove valuable in obtaining relevant information used to assess sleep quality, but require patients to have electrodes attached to their bodies.
In an effort to provide a less intrusive way to study sleep on a longer-term basis, actigraphs have been developed. These devices can be attached to any of the limbs and provide movement data based on the same principles behind accelerometers. They are also used in activity studies and can provide twenty-four hour monitoring of the subject. This type of sensor, however, has its limitations in acquiring data that can be interpreted definitively to provide a good assessment of sleep quality. Researchers are dependent on patient journals to help correlate the data recorded on the actigraph and it is hard to distinguish different events that can occur throughout the night. Patient non-compliance in journaling adds to the confusion. In addition, many of the problems researchers have interpreting results from actigraphs are a direct result of the one-dimensional nature of the data recorded. For example, if a patient places their hand on their chest or under their head, the motion data recorded by the actigraph can be misinterpreted or could potentially hide important events.
To provide an even less intrusive approach that does not involve equipment attached to the subject, different physiological parameters must be examined. One way to look at cardiologic and respiratory events is through a technique called ballistocardiography (BCG). BCG involves the study of the cardiac system by measuring forces related to the contraction and relaxation of the heart, along with forces propagated throughout the vascular system. It has been shown that cardiac forces correlate to life duration and susceptibility to ischemic heart disease. Additionally, it has been shown that the average force seen in the BCG reduces as a person ages. Unfortunately, initial high expectations and hopes set forth in the mid-20th century for this technology simply resulted in disappointments because inadequate analysis tools were available. In addition, the electrocardiogram (ECG) quickly usurped this technology as a more practical way to measure cardiac function.
Now that data acquisition systems are commonplace and the cost of personal computers have been greatly reduced in the last decade, analysis of the BCG data is no longer an insurmountable hurdle. In fact, a team from Stanford University designed a system called “SleepSmart” that uses an array of pressure and temperature sensors to acquire physiological data. These sensors are embedded in a mattress and can detect position, temperature, sound, vibration and movement, with other sensors optional for additional information. They determined that a sheet of piezoelectric film was best for implementation of their design. However, they were only able to obtain results similar to a static charge sensitive bed in that they were able to obtain good measures of breathing waveforms, but unable to obtain reliable heart rate measures, thus deeming the technology insufficient for medical application.
Previous efforts to passively, i.e., without the active involvement of the subject and without direct connection of sensors to the subject, collect physiological data have been expensive, as processing ability for analysis sufficient for medical applications was inefficient and expensive. Isolating the appropriate components from a signal required complicated circuitry and processing. This complexity added expense without a proportional increase in accuracy and reliability of the outputs. Due to the number of people that suffer from sleep related disorders, as well as the need for non-invasive collection of medical and other data in applications such as, monitoring the conditions of sick, old or bedridden patients and prenatal infants, there is need for a more efficient and accurate passive data collection and analysis system to monitor various conditions of subjects. In particular, there is a need for such a passive system in hospitals, nursing homes, assisted living facilities, sleep labs or home sleep study centers, doctors' offices, health monitoring stations and the like.